# US 2019/0244136 A1 · Inter-sensor learning > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2019/0244136 A1 - Document ID: US20190244136A1 - Status: Published application - Kind: A1 - Application number: 15/888,322 - Filed: 2018-02-05 - Published: 2019-08-08 - Earliest claimed: 2018-02-05 - Assignees: GM Global Technology Operations LLC - Applicants: GM Global Technology Operations LLC - Inventors: Yasen Hu; Shuqing Zeng; Wei Tong; Mohannad Murad; Gregg R. Kittinger; David R. Petrucci - Web page: https://patentgrep.com/patent/US20190244136A1 ## CPC - G06N 20/00: Machine learning - G05D 1/0238: {using obstacle or wall sensors (G05D1/0246 and G05D1/0289 take precedence; lidar systems designed for anti-collision purposes G01S17/93)} - G05D 1/0255: {using acoustic signals, e.g. ultra-sonic singals (sonar systems designed for anti-collision purposes G01S15/93)} - G06N 3/0464: Convolutional networks [CNN, ConvNet] - G06N 3/09: Supervised learning - G06V 10/70: using pattern recognition or machine learning (optical pattern recognition or electronic computations therefor G06V10/88) - G06V 20/58: Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads - G06V 40/70: Multimodal biometrics, e.g. combining information from different biometric modalities - B60W 40/02: related to ambient conditions - G06N 3/045: Combinations of networks - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G05D: SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING - B60W: CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT ## Abstract A system and method to perform inter-sensor learning obtains a detection of a target based on a first sensor. The method also includes determining whether a second sensor with an overlapping detection range with the first sensor also detects the target, and performing learning to update a detection algorithm used with the second sensor based on the second sensor failing to detect the target. ## Claims ### Claim 1 A method of performing inter-sensor learning, the method comprising: obtaining a detection of a target based on a first sensor; determining whether a second sensor with an overlapping detection range with the first sensor also detects the target; and performing learning to update a detection algorithm used with the second sensor based on the second sensor failing to detect the target. ### Claim 2 (depends on 1) The method according to claim 1, wherein the performing the learning is offline. ### Claim 3 (depends on 2) The method according to claim 2, further comprising performing online learning to reduce a threshold of detection by the second sensor prior to the performing the learning offline. ### Claim 4 (depends on 3) The method according to claim 3, further comprising logging data from the first sensor and the second sensor to execute the performing the learning offline based on the performing online learning failing to cause detection of the target by the second sensor. ### Claim 5 (depends on 1) The method according to claim 1, further comprising determining a cause of the second sensor failing to detect the target and performing the learning based on determining that the cause is based on the detection algorithm. ### Claim 6 (depends on 1) The method according to claim 1, wherein the performing the learning includes a deep learning. ### Claim 7 (depends on 1) The method according to claim 1, wherein the obtaining the detection of the target based on the first sensor includes a microphone detecting the target. ### Claim 8 (depends on 7) The method according to claim 7, wherein the determining whether the second sensor also detects the target includes determining whether a camera also detects the target. ### Claim 9 (depends on 1) The method according to claim 1, wherein the obtaining the detection of the target based on the first sensor and the determining whether the second sensor also detects the target is based on the first sensor and the second sensor being disposed in a vehicle. ### Claim 10 (depends on 9) The method according to claim 9, further comprising augmenting or automating operation of the vehicle based on the detection of the target. ### Claim 11 A system to perform inter-sensor learning, the system comprising: a first sensor, wherein the first sensor is configured to detect a target; a second sensor, wherein the second sensor has an overlapping detection range with the first sensor; and a processor configured to determine whether the second sensor also detects the target and perform learning to update a detection algorithm used with the second sensor based on the second sensor failing to detect the target. ### Claim 12 (depends on 11) The system according to claim 11, wherein the processor is configured to perform the learning is offline. ### Claim 13 (depends on 12) The system according to claim 12, wherein the processor is further configured to perform online learning to reduce a threshold of detection by the second sensor prior to performing the learning offline. ### Claim 14 (depends on 13) The system according to claim 13, wherein the processor is further configured to log data from the first sensor and the second sensor to perform the learning offline based on the online learning failing to cause detection of the target by the second sensor. ### Claim 15 (depends on 11) The system according to claim 11, wherein the processor is further configured to determine a cause of the second sensor failing to detect the target and perform the learning based on determining that the cause is based on the detection algorithm. ### Claim 16 (depends on 11) The system according to claim 11, wherein the learning includes deep learning. ### Claim 17 (depends on 11) The system according to claim 11, wherein the first sensor is a microphone. ### Claim 18 (depends on 17) The system according to claim 17, wherein the second sensor is a camera. ### Claim 19 (depends on 11) The system according to claim 11, wherein the first sensor and the second sensor are disposed in a vehicle. ### Claim 20 (depends on 19) The system according to claim 19, wherein the processor is further configured to augment or automate operation of the vehicle based on the detection of the target.